BLENDED VIIRS+MICROWAVE ICE CONCENTRATION€¦ · STAR JPSS Annual Science Team Meeting, 27 - 30 August 2018 1 BLENDED VIIRS+MICROWAVE ICE CONCENTRATION Yinghui Liu1, Jeff Key2,and

1STAR JPSS Annual Science Team Meeting, 27 - 30 August 2018

BLENDEDVIIRS+MICROWAVE

ICE CONCENTRATIONYinghui Liu1, Jeff Key2,and Richard Dworak1

1Cooperative Institute for Meteorological Satellite Studies2NOAA/NESDIS Madison, WI


Motivation: all-sky high resolution

Passive microwave ice concentration:Con: low spatial resolutionPro: all-weather

Passive infrared/visible ice concentration:Con: clear-sky onlyPro: high spatial resolution

AMSR-2 ice concentration 06-24-2015

S-NPP VIIRS ice concentration 06-24-2015


Algorithm Overview: approach

The Best Linear Unbiased Estimator (BLUE) is then applied to derive the final ice concentration under clear sky conditions:

where ICE_CONC, ICE_CONC1, and ICE_CONC2, are optimized ice concentration, and ice concentrations from the two products; D1 and D2 are measurement accuracy; σ1 and σ2 are the measurement precision.

ICE_CONC=(s 22

s12 +s 2

2) (́ICE_CONC1 - D1)+(

s12

s12 +s 2

2) (́ICE_CONC2 - D2 )


Algorithm Overview: parameters

Comparison of VIIRS and Landsat ice concentrations for different concentration ranges/bins. Also shown are the differences overall (upper left) and the bias and root-mean-square (RMS) difference as a function of VIIRS ice concentration (bottom row).

Same comparisons are made for AMSR2 ice concentration.


Algorithm Overview: case study

Blended sea ice concentration from Microwave and infrared/visible

Passive microwave ice concentration:Con: low spatial resolutionPro: all-weather

Passive infrared/visible ice concentration:Con: clear-sky onlyPro: high spatial resolution

Blended ice concentration:high spatial resolution under all-weather conditions

Blended sea ice concentration at 1 km resolution on June 24, 2015 using AMSR-2 and the Suomi NPP VIIRS ice concentration products


Performance

On May 11, 2017 over Baffin Bay VIIRS, AMSR2 and Blended SIC on top. Landsat-8 OLI/TIRS and SAR Sentinel-1B imagery on bottom


Performance

On May 27, 2017 near Alaskan Beaufort Sea Coast VIIRS, AMSR2 and Blended SIC on top. Landsat-8 OLI/TIRS and SAR Sentinel-1A imagery on bottom


Daily Ice concentration composite from VIIRS (left); and AMSR2 (right) over the Arctic on March 5th 2017.

Current Status• Blended ice concentration is being generated daily for National Ice Center• Data is in GeoTIFF format, over both Arctic and Antarctic


Blended Daily Ice concentration (IC) over the Arctic on March 5th 2017.

Current Status• Blended ice concentration is being generated daily for National Ice Center• Data is in GeoTIFF format, over both Arctic and Antarctic


User Interaction

Blended ice concentration is currently archived for National Ice Center for evaluation


Summary and Path Forward• Blended ice concentration from VIIRS and passive microwave provides

high spatial resolution ice concentration under all-weather conditions;

• This product can benefit operational applications, and long-term scientific studies;

• Further improvement and evaluation is needed with new ice concentration products from sensors with very high spatial resolution, e.g. SAR.

Labrador Sea, May 3, 2013

Ice/water retrievals at 5 km spacing

Ice agrees with IMS

Water agrees with IMS

Water disagrees with IMS

Ice disagrees with IMS

Acknowledgement: Mark Buehner, Alex Komarov, and Alain Caya (ECCC)

Sean Helfrich (STAR)


BLENDEDAMSR2+VIIRS

SEA ICE MOTIONAaron Letterly, CIMSS, University of Wisconsin-Madison

[email protected]

Collaborators: Jeff Key, Yinghui Liu


Algorithm Overview

• Automated sea ice motion uses a pair of satellite images to determine the displacement of ice features under clear sky conditions.

• The algorithm outputs motion as a velocity in the u- or v-direction. Velocity is based on the number of grid cells “travelled” between the image pair.

• Grid cell size is based on the spatial resolution of the imagery

• Blended sea ice motion combines information from multiple sensors at different resolutions


Algorithm: Blending AMSR2+ VIIRS

• Objective: Combine the all-weather capabilities of AMSR2 89GHz channel with the precision of VIIRS M-bands

• Difficulties: Spatial resolution of AMSR2 ~9x more coarse (6.25km) than VIIRS (0.75km)

• Solution: Regrid both imagesto a shared resolution (~1km) so that motion output can be combined


Algorithm: Blending AMSR2+VIIRS

• Input from both sensors is regrid from swaths onto a polar stereographic grid

• MODIS Swath to Grid Transfer (MS2GT) does the hard work

• Once the inputs are at the same resolution, ice motion is calculated, then combined

• Simple combination technique (arithmetic mean)


Algorithm: Blending AMSR2+ VIIRS

• Once both grids have been remapped to a common grid, features and motion can be compared and combined

• Adding in a third input (SAR) could provide more information, greater challenges


Motion from all-weather AMSR2 output (left) and high-resolution VIIRS M15 output (right)

Performance: Blending AMSR2+ VIIRS


Blended product provides high spatial resolution under all-weather conditions, spatially coherent vectors

Performance: Blending AMSR2+ VIIRS


Summary and Path Forward

• Blended ice motion from VIIRS and AMSR2 provides high spatial resolution ice motion under all-weather conditions

• Demonstrate capabilities of multi-channel VIIRS ice motion (Day-Night Band) and include data from SAR (at 250m resolution!)

• Product benefits operational applications, and more sensor input can provide a more consistent record of ice motion

• Procedure for combining vectors is too simplistic. Known error and sensitivity of ice motion from each sensor input should computed and considered for future applications

Documents

BLENDED VIIRS+MICROWAVE ICE CONCENTRATION€¦ · STAR JPSS Annual Science Team Meeting, 27 - 30 August 2018 1 BLENDED VIIRS+MICROWAVE ICE CONCENTRATION Yinghui Liu1, Jeff Key2,and